





import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowercase	: Any       ="""▁"""

__lowercase	: str       =get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
class   A							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  =BertGenerationTokenizer
      _snake_case				  =False
      _snake_case				  =True



      def 						lowerCAmelCase__     (						self:    int							)    ->     Dict:
         '''simple docstring'''

         super().setUp()

         UpperCAmelCase_           =BertGenerationTokenizer(_lowerCAmelCase		,	keep_accents=_lowerCAmelCase							)
         tokenizer.save_pretrained(self.tmpdirname							)



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           ="<s>"
         UpperCAmelCase_           =1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase							)		,	_lowerCAmelCase							)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase							)		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =list(self.get_tokenizer().get_vocab().keys()							)

         self.assertEqual(vocab_keys[0]		,	"<unk>"							)
         self.assertEqual(vocab_keys[1]		,	"<s>"							)
         self.assertEqual(vocab_keys[-1]		,	"<pad>"							)
         self.assertEqual(len(_lowerCAmelCase							)		,	1002							)



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Dict:
         '''simple docstring'''

         self.assertEqual(self.get_tokenizer().vocab_size		,	1000							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =BertGenerationTokenizer(_lowerCAmelCase		,	keep_accents=_lowerCAmelCase							)

         UpperCAmelCase_           =tokenizer.tokenize("This is a test"							)
         self.assertListEqual(_lowerCAmelCase		,	["▁This", "▁is", "▁a", "▁t", "est"]							)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_lowerCAmelCase							)		,	[285, 46, 10, 170, 382]		,	)

         UpperCAmelCase_           =tokenizer.tokenize("I was born in 92000, and this is falsé."							)
         self.assertListEqual(
             _lowerCAmelCase		,	[
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]		,	)
         UpperCAmelCase_           =tokenizer.convert_tokens_to_ids(_lowerCAmelCase							)
         self.assertListEqual(
             _lowerCAmelCase		,	[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,	)

         UpperCAmelCase_           =tokenizer.convert_ids_to_tokens(_lowerCAmelCase							)
         self.assertListEqual(
             _lowerCAmelCase		,	[
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]		,	)



      @cached_property
      def 						lowerCAmelCase__     (						self:    str							)    ->     Optional[Any]:
         '''simple docstring'''

         return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"							)



      @slow
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           ="Hello World!"
         UpperCAmelCase_           =[1_8536, 2260, 101]

         self.assertListEqual(_lowerCAmelCase		,	self.big_tokenizer.encode(_lowerCAmelCase							)							)



      @slow
      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =(
             "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
             " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
         )
         UpperCAmelCase_           =[
             871,
             419,
             358,
             946,
             991,
             2521,
             452,
             358,
             1357,
             387,
             7751,
             3536,
             112,
             985,
             456,
             126,
             865,
             938,
             5400,
             5734,
             458,
             1368,
             467,
             786,
             2462,
             5246,
             1159,
             633,
             865,
             4519,
             457,
             582,
             852,
             2557,
             427,
             916,
             508,
             405,
             3_4324,
             497,
             391,
             408,
             1_1342,
             1244,
             385,
             100,
             938,
             985,
             456,
             574,
             362,
             1_2597,
             3200,
             3129,
             1172,
         ]

         self.assertListEqual(_lowerCAmelCase		,	self.big_tokenizer.encode(_lowerCAmelCase							)							)



      @require_torch
      @slow
      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     Any:
         '''simple docstring'''

         import torch

         from transformers import BertGenerationConfig, BertGenerationEncoder

         # Build sequence
         UpperCAmelCase_           =list(self.big_tokenizer.get_vocab().keys()							)[:10]
         UpperCAmelCase_           =" ".join(_lowerCAmelCase							)
         UpperCAmelCase_           =self.big_tokenizer.encode_plus(_lowerCAmelCase		,	return_tensors="pt"		,	return_token_type_ids=_lowerCAmelCase							)
         UpperCAmelCase_           =self.big_tokenizer.batch_encode_plus(
             [sequence + " " + sequence]		,	return_tensors="pt"		,	return_token_type_ids=_lowerCAmelCase							)

         UpperCAmelCase_           =BertGenerationConfig()
         UpperCAmelCase_           =BertGenerationEncoder(_lowerCAmelCase							)

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
            model(**_lowerCAmelCase							)
            model(**_lowerCAmelCase							)






      @slow
      def 						lowerCAmelCase__     (						self:    str							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           ={"input_ids": [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCAmelCase		,	model_name="google/bert_for_seq_generation_L-24_bbc_encoder"		,	revision="c817d1fd1be2ffa69431227a1fe320544943d4db"		,	)










'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


